In this project research is conducted to characterize and develop new animal models of human disease and to develop the means to better characterize a model's relevance for human disease, addressing critical barriers to research progress. Additional aims include the the development of new research technologies for the evaluation and application of disease biomarkers. Progress was made in developing cancer diagnostics and in research resources useful in developing and characterizing new models of human cancer. This research project included developing capabilities in molecular diagnostics for cancer models, developing methods for automated morphmetric image analysis of cancer specimens for quantitative pathology, investigating the role of S100 in cancer, developing new methods in mass spectrometry for limited tissue such as biopsies or model animals, and effects of Ras oncogene activation. Continued advances and applications in developing quality assurance methods for tissue biobanking were also made. Biorepository supported translational research depends upon high-quality, well-annotated specimens. Histopathology assessment contributes insight into how representative lesions are for research objectives. Feasibility of documenting histological proportions of tumor and stroma was studied in an effort to enhance information regarding biorepository tissue heterogeneity. Unique spatial-spectral image analysis algorithms were developed for applying automated pattern recognition morphometric image analysis to quantify histologic tumor and non-tumor tissue areas in biospecimen tissue sections. Successfully acquired measurements for lymphomas, osteosarcomas and melanomas were extended to include additional progress in developing and validating algorithms for cancers of the blood and vascular tissues, lung, and connective mesenchymal tissues (soft tissue sarcoma). Quantitative image analysis automation is anticipated to minimize variability associated with routine biorepository pathologic evaluations and enhance biomarker discovery by helping to guide the selection of study-appropriate specimens. Pattern recognition image analysis (PRIA) is artificial intelligence automation technology that has the capacity to significantly impact the work that pathologists do when applied to histopathology. To what degree computer-assisted diagnostic pattern recognition image analysis agrees with accepted histopathology approaches has not been clearly established. Digitally scanned histomorphological whole-slide images from two sources served for evaluation of a commercially available pattern recognition image analysis platform, to address diagnostic agreement achievable. Substantial agreement, lacking significant constant or proportional errors, between pattern recognition image analysis and manual morphometric image segmentation was obtained for pulmonary metastatic cancer areas (Passing/Bablok regression). Bland-Altman analysis indicated heteroscedastic measurements and tendency toward increasing variance with increasing tumor burden, but no significant trend in mean bias. The average between-methods percent tumor content difference was -0.64. Analysis of between-methods measurement differences relative to the percent tumor magnitude revealed that method disagreement had an impact primarily in the smallest measurements (tumor burden 0.988, indicating high reproducibility for both methods, yet pattern recognition image analysis reproducibility was superior (C.V.: PRIA = 7.4, manual = 17.1). Evaluation of pattern recognition image analysis on morphologically complex teratomas led to diagnostic agreement with pathologist assessments of pluripotency on subsets of teratomas. Accommodation of the diversity of teratoma histologic features frequently resulted in detrimental trade-offs, increasing pattern recognition image analysis error elsewhere in images. Pattern recognition image analysis error was nonrandom and influenced by variations in histomorphology. File-size limitations encountered while training algorithms and consequences of spectral image processing dominance contributed to diagnostic inaccuracies experienced for some teratomas. Pattern recognition image analysis appeared better suited for tissues with limited phenotypic diversity. Technical improvements may enhance diagnostic agreement, and consistent pathologist input will benefit further development and application of pattern recognition image analysis.Quantitative image analysis pathology was employed to study chloride intracellular channel (CLIC) 4. CLIC 4 is a member of a redox-regulated, metamorphic multifunctional protein family, first characterized as intracellular chloride channels. Current knowledge indicates that CLICs participate in signaling, cytoskeleton integrity and differentiation functions of multiple tissues. Image analysis algorithms were developed and applied to obtain evidence of nuclear localization and quantitation results supporting the indication that CLIC4 suppresses the growth of squamous cancers, that reduced CLIC4 expression and nuclear residence detected in cancer cells is associated with the altered redox state of tumor cells, and the absence of detectable nuclear CLIC4 in cancers contributes to TGF-beta resistance and enhances tumor development.In vivo image analysis provided further means to document enhanced CD8 T cell-mediated therapeutic efficacy using a combination regimen of murine IL-15 administered with an agonistic anti-CD40 Ab (FGK4.5), which led to increased IL-15Ralpha expression on dendritic cells (DCs), as well as other cell types, in a syngeneic established TRAMP-C2 tumor model. IL-15 has potential as an immunotherapeutic agent for cancer treatment because it is a critical factor for the proliferation and activation of NK and CD8(+) T cells. Anti-CD40-mediated augmented IL-15Ralpha expression was critical in IL-15-associated sustained remissions observed in TRAMP-C2 tumor-bearing mice receiving combination therapy.The significant materials, equipment or methods in this project include use of recombinant DNA technology, in vitro cell culture, DNA sequence analysis, immunodiagnostics, molecular imaging, morphometrics, computer assisted image analysis, optical imaging, mass spectrometry, molecular pathology, and veterinary medical diagnosis.